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Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

Saeed, Umer; Shah, Syed Yaseen; Ahmad, Jawad; Imran, Muhammad Ali; Abbasi, Qammer H.; Shah, Syed Aziz


Umer Saeed

Syed Yaseen Shah

Muhammad Ali Imran

Qammer H. Abbasi

Syed Aziz Shah


The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the coronavirus disease 2019 (COVID-19) pandemic, has affected more than 400 million people worldwide. With the recent rise of new Delta and Omicron variants, the efficacy of the vaccines has become an important question. The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions, particularly for healthcare workers. In this paper, we discuss the current literature on invasive/contact and non-invasive/non-contact technologies (including Wi-Fi, radar, and software-defined radio) that have been effectively used to detect, diagnose, and monitor human activities and COVID-19 related symptoms, such as irregular respiration. In addition, we focused on cutting-edge machine learning algorithms (such as generative adversarial networks, random forest, multilayer perceptron, support vector machine, extremely randomized trees, and k-nearest neighbors) and their essential role in intelligent healthcare systems. Furthermore, this study highlights the limitations related to non-invasive techniques and prospective research directions.

Journal Article Type Review
Acceptance Date Dec 30, 2021
Online Publication Date Jan 5, 2022
Publication Date 2022-04
Deposit Date Jul 15, 2022
Publicly Available Date Jul 15, 2022
Journal Journal of Pharmaceutical Analysis
Print ISSN 2095-1779
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 12
Issue 2
Pages 193-204
Keywords Artificial intelligence, Non-invasive healthcare, Machine learning, Non-contact sensing, COVID-19
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